This paper proposes a new optimization framework for energy distribution systems using modified PSO, inspired by the flocking behavior of avian species. In this respect, the research study will be focused on energy ge...
详细信息
This paper proposes a new optimization framework for energy distribution systems using modified PSO, inspired by the flocking behavior of avian species. In this respect, the research study will be focused on energy generation and consumption optimization in different energy hubs with diverse generation sources and demand profiles. One of the key features to be included is the integration of WT-power production, energy demand, and price data into an overall model. By making use of historical wind speed data in conjunction with the Monte Carlo simulation technique, the model generates 1000 production scenarios that undergo a filtering process to select just three of the most likely scenarios using the Kantorovich distance matrix method. It therefore makes the generated scenarios at least 25% more accurate than those from traditional models. Its PSO algorithm will lead the way through complex optimization landscapes and come up with as much as a 35% reduction in operational costs when compared to conventional models. Besides this, the production of energy under this model aligns better with demand, thus reducing the mismatch between energy supply and demand by 20%. The results therefore show that the PSO-based model significantly yields a more efficient, cost-effective energy system. This work thus gives theoretical inputs into energy optimization with practical solutions to economically feasible energy distribution systems. Results recommend the best way to utilize energy resources in realistic applications, thus constituting a theoretically and practically valuable development in energy resource management.
Accurate traffic flow forecasting is an important technical measure to alleviate traffic congestion. Since traffic flow has spatial and temporal characteristics, thus the adequate extraction of its spatio-temporal fea...
详细信息
Accurate traffic flow forecasting is an important technical measure to alleviate traffic congestion. Since traffic flow has spatial and temporal characteristics, thus the adequate extraction of its spatio-temporal features is an important prerequisite to promote the forecast accuracy of the model. However, a majority of existing traffic flow prediction models cannot sufficiently consider the neighborhood spatio-temporal relationship for the real road network in the modeling process, which makes it difficult to improve the model prediction accuracy. For this reason, this paper takes improved feature enhancement graph convolution (FEGC), gated recurrent unit (GRU), and improved lightweight particleswarmoptimization (ILPSO) algorithm as components, respectively, to construct a combinatorial traffic flow prediction model FEGCGRU-ILPSO (FGI), aiming to achieve accurate forecast for regional traffic flow through fully learning spatio-temporal correlation characteristics. Firstly, considering that most traffic flow modeling methods are ineffective in characterizing the hidden association information within nodes, we propose the method of constructing the virtual space adjacency matrix based on the improved gray relational analysis (IGRA) algorithm, which achieves the effective characterization of road network neighborhood relationship by fusing it with the original adjacency matrix. Then, based on the idea of matrix decomposition, the weight adjacency matrix is further introduced to realize the dynamic capture of time -varying correlation of node graph structure in realistic road networks. Secondly, to address the performance degradation problem caused by feature assimilation in multi -layer graph convolution, an improved feature enhancement graph convolution component is proposed to alleviate the multi -layer graph convolution oversmoothing by enhancing salient features. Finally, considering the convex optimization problem caused by the way the hyperparameters of the mo
The low ambient pressure during the flight of aircraft has a significant impact on the performance and safety of the onboard power battery. In order to ensure the safe operation of the battery system, it is very impor...
详细信息
The low ambient pressure during the flight of aircraft has a significant impact on the performance and safety of the onboard power battery. In order to ensure the safe operation of the battery system, it is very important to accurately estimate and manage the state-of-charge (SOC) of the battery. In this work, the equivalent circuit modeling (ECM) of lithium titanate battery (LTB) is studied in detail, and the influence of analog circuit model parameters in low ambient pressures is discussed for the first time. The forgetting factor recursive least square algorithm is introduced to accurately identify the ECM parameters of the LTB under different pressures online. The particle swarm optimization algorithm is innovatively proposed to optimize the covariance matrix of the Kalman filter algorithm. The verification shows that the root mean square error of the ECM of LTB under different ambient pressures is less than 0.025. In the SOC estimation process, the noise covariance matrixes of the extended Kalman filter and the unscented Kalman filter are optimized by the particleswarmalgorithm. The optimized SOC estimation absolute error is less than 3 %, especially at 96 kPa and 30 kPa, where the absolute error is less than 2 %.
Image Segmentation using thresholding is one of the most significant areas of image processing. However, the challenge lies in accurately and effectively segmenting medical images, which is a crucial step in many appl...
详细信息
Image Segmentation using thresholding is one of the most significant areas of image processing. However, the challenge lies in accurately and effectively segmenting medical images, which is a crucial step in many applications of medical image analysis. It necessitates the development of an effective and robust segmentation approach that can handle the complexity and diversity of medical images. To address this problem, we propose a novel image segmentation technique based on minimizing the cross-entropy function using a hybrid approach that combines the features of Opposition-Based Learning (OBL), Chameleon swarmalgorithm (CSA), and particle swarm optimization algorithm (PSO). The opposition-based technique generates the initial population and improves convergence. Then, PSO and CSA are run in parallel on an unequal population set to improve the optimal results. The proposed approach, named the Opposition-based Chameleon swarmalgorithm improved by particleswarmalgorithm (CSAPSO), is evaluated on twelve Chest X-Ray (CXR) images of patients for the detection of Pneumonia. It is further tested on a large data set related to COVID-19. We conducted extensive comparisons with other state-of-the-art methods and the Deep Learning algorithms and used the performance indicators, namely Root Mean Square Error (RMSE), Peak Signal to Noise Ratio (PSNR) and Structure Similarity Index (SSIM), Classification Accuracy, Area Under Curve for evaluating the performance. The proposed approach is statically analyzed using the Friedman rank-sum test. Through the analysis, CSAPSO demonstrates better global optimal results compared to state-of-the-art techniques.
Droughts typically develop gradually, and early prediction is crucial for the government to formulate effective mitigation plans. Our approach does not involve predicting specific drought index values. Instead, we for...
详细信息
Droughts typically develop gradually, and early prediction is crucial for the government to formulate effective mitigation plans. Our approach does not involve predicting specific drought index values. Instead, we forecast whether a particular year will experience drought. Insufficient investigation has been carried out regarding variations in additional climatic indicators like shortwave radiation, wind speed, sea level, and pollution in the context of droughts in the state of Tamil Nadu, India. In the study period taken from 1995 to 2020, only three years (2002, 2009, and 2017) experienced drought occurrences, resulting in an imbalanced dataset. To enhance the classification performance of this imbalanced dataset, a weighted dataset is constructed using a feature weighting approach known as the Single Objective Scorer (SOS) based Multi-objective PSO(MPSO) in conjunction with the Gradient Boosting Classifier. The proposed model facilitates objective-based multi-population formation and neighborhood learning. Precision and recall are crucial metrics, particularly in measuring imbalanced dataset classification performance. The application of multi-objective optimization techniques helps to strike a suitable balance between precision and recall. In addition to the Standardized Precipitation Index (SPI) and Standardized Precipitation Evapotranspiration Index (SPEI), 14 climatic indicators based on land, atmosphere, and sea are utilized. By employing the weighted dataset created with SOS-based MPSO, a significant improvement in recall value of 0.81 is achieved. Based on the weights assigned to the features, it is identified that the Mean Sea Level of the Arabian Sea and CO2 are significant indicators for predicting meteorological drought. The Explainable AI techniques SHAP and LIME are employed for interpreting the drought prediction model, providing insights into its workings.
In traditional temperature control system of particle 3D printing extrusion device, it exists many issues such as slow response speed, large fluctuation, and poor anti-interference ability. The above issues cause the ...
详细信息
In traditional temperature control system of particle 3D printing extrusion device, it exists many issues such as slow response speed, large fluctuation, and poor anti-interference ability. The above issues cause the instability of the extruded PLA filament, affecting the mechanical properties and surface quality of the printed samples. Based on particle swarm optimization algorithm, this paper proposes a precise control method for temperature control system to iteratively optimize the quantization factor and proportion factor, finally it obtains the optimal weight factor. Compared with traditional PID control and fuzzy PID control, response speed has improved by 58.6 % and 40.0 % respectively, overshoot has reduced by 76 % and 35 % respectively, and steady-state time has shortened to 24 s. Comparison of experimental results: The tensile strength of the samples increases by 15.52 % and 7.47 % respectively, the bending strength increases by 17.93 % and 11.58 % respectively, and the internal pores are improved significantly. In summary, the method proposed in this paper can effectively solve the problems of the temperature control system for particle 3D printing, and improve the mechanical properties and surface quality of the samples. The printed prosthetic orthotic plate can well meet the fitness and comfort of the human body.
Conformable fractional-order grey prediction models have attracted considerable attention due to their versatile modeling techniques. However, existing models often suffer from limitations in adaptability. To address ...
详细信息
Conformable fractional-order grey prediction models have attracted considerable attention due to their versatile modeling techniques. However, existing models often suffer from limitations in adaptability. To address this, this study proposes a new extended conformable fractional-order grey prediction model, namely the ECFGM(1,1) model. By integrating an adaptive weighting coefficient into the conformable fractional-order accumulation process, the model can effectively prioritize new information, thereby enhancing its rationality and adaptability. Moreover, the adjusted process can be tailored to either emphasize new information or adhere to traditional accumulation methods, which improves its adaptability. To verify the effectiveness of the ECFGM(1,1) model, ECFGM(1,1) is applied to two examples from the literature. The model evaluation results show that the ECFGM(1,1) model has higher fitting accuracy and predictive accuracy than the GM(1,1), CFGM(1,1), and NIPGM(1,1) models. Using the constructed ECFGM(1,1) for predictive analysis of the per capita electricity consumption for daily life in China, the results show that this model can capture the laws of its changes over time. Finally, per capita electricity consumption for daily life in China from 2022 to 2026 is predicted. The results show that by 2026, such consumption is estimated to reach 1165.35 KWh.
In battery management systems, the health status of lithium batteries constantly affects the accurate estimation of their charging status and energy status, making the health status particularly important. However, in...
详细信息
In battery management systems, the health status of lithium batteries constantly affects the accurate estimation of their charging status and energy status, making the health status particularly important. However, in energy storage systems, rapidly and accurately estimating the health status of lithium batteries, as well as estimating it at an appropriate proportion, has always been a challenge. Therefore, to improve the stable operation of energy storage systems, this paper proposes a model that optimizes a hybrid kernel extreme learning machine using the circle mapping chaotic method and particle swarm optimization algorithm to improve the gray wolf algorithm. First, to address the issues of initialization instability and slow convergence speed in the gray wolf optimizationalgorithm, the ideas of circle mapping chaos and particleswarmoptimization are proposed to replace the position update formula of the gray wolf optimizationalgorithm, thereby enhancing the algorithm's stability and convergence speed. Second, to address the limitation of the single learning capability of the kernel extreme learning machine model, a hybrid kernel extreme learning machine model is employed. The generalization and learning capabilities of the entire model are verified through simulation experiments. Finally, simulation predictions are conducted again under different data-splitting proportions to obtain an optimal training proportion, providing additional reference directions for practical application. Experimental results demonstrate that the proposed model maintains a fit above 0.99 for four sets of batteries, and ensures the reliability and reasonableness of the model when using a 50% training set proportion.
With the increasing adoption of electric vehicles, the limitations of BMS in terms of storage capacity and computational power lead to a gradual accumulation of errors in battery capacity estimation over time. This gr...
详细信息
With the increasing adoption of electric vehicles, the limitations of BMS in terms of storage capacity and computational power lead to a gradual accumulation of errors in battery capacity estimation over time. This growing inaccuracy significantly compromises the effective management of on-board power battery states. The challenge of battery capacity estimation based on extensive cloud-stored data has become a key focus in current research. In this paper, we propose an enhanced method that combines the ECM with a RNN to address this issue. By incorporating the relationship between OCV and SOC into the ECM, and employing PSO for direct capacity identification, we achieve accurate estimation. Furthermore, the use of RNN dynamically adjusts the observation noise in Kalman filtering, significantly improving the precision of the estimation. Experimental results demonstrate that the proposed `method yields a RMSE of <3 % and an average relative error below 2 %, compared to traditional approaches. This study presents a high-precision, efficient solution for estimating battery capacity using cloud-based data from electric vehicles, offering substantial application value.
Aiming to solve the problem of odor source localization (OSL) in the presence of interference sources, this paper presents two methods based on swarm intelligence algorithms. We initially introduced the shark smell op...
详细信息
Aiming to solve the problem of odor source localization (OSL) in the presence of interference sources, this paper presents two methods based on swarm intelligence algorithms. We initially introduced the shark smell optimization (SSO) algorithm and modified it for OSL tasks. Subsequently, mechanisms for collective information sharing and preventing falling into local minima were incorporated, leading to the development of the Improved Shark Smell optimization (I-SSO) algorithm. We tested both algorithms in a computational fluid dynamics (CFD) simulated environment with a single interference source and compared them to the particleswarmoptimization (PSO) algorithm and whale optimizationalgorithm (WOA). In scenarios with one and two interference sources. The results showed that the I-SSO algorithm outperformed the other three algorithms in both environment settings, demonstrating a higher success rate and superior search distance efficiency.
暂无评论